Trends and financial management of the analytics processes

1. Using Financial Analytics for
Performance Management
Presentation to the Pittsburgh Association for Financial Professionals
Richard Gristak Sr. Practice Dir. Business Analytics
Ciber Inc.
November 21, 2013

2. 2
Roadmap
Ciber Intro
Trends Today
The Data Dilemma
What are the Business Goals?
A Framework for a BI Support Center
Analytics and Predictive Analytics
Metadata and Contextual Analysis
Governance Issues
Some vendor product stacks

3. 3
About Ciber
A $1.1 billion Global IT services company that builds, integrates and supports
applications and infrastructures for business and government in 72 offices and 19
countries.
Founded in 1974
More than 7,000 employees
NYSE: CBR - Headquartered in Denver
72 Offices in 19 countries
Local Accountability with Global Delivery:
Domestic & Off-Shore
Growth & Profitability for 35 years
Focus on Quality: ISO, CPMM, SAS 70
Best-in-Class Client Satisfaction
As a result of an independent customer satisfaction
survey, asking more than 150 international CIBER
customers, 96% respond that they will choose CIBER
again.

4. 4
Core Capabilities & Services
Strategy
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Development of IT Strategic Plans
Design and Implementation of IT
Governance Frameworks
Design, Evaluation and
Implementation of IT operating
Models
Design and implementation of
Portfolio Management (Project &
Application)
Development of IT Business Cases
Cloud Adoption Strategies
Project Management & PMO
 Project Management
 Process Definition
 Complete PMO solution offering
Enterprise Architecture
Enterprise Architecture Program Design and
Implementation
Business Architecture Development
SOA Strategy, Roadmap, Governance
Data /Information Governance
System & Technology Roadmap
Long Term Technology Planning
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Application
Development
 Design, build, test & implement
custom applications
 Application modernization &
enhancement
 Collaboration
 Mobility & wireless
 Portal development
 Document Management and
Enhanced ECM
 Web 2.0 and Social Media
 Records/Contract Management
 Platform Integration and
Consolidation
BI/DW
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Data Warehousing
Data Architecture
Business Intelligence Systems
SAP Business Objects
Master Data Management
(MDM)
Data Governance
Common Data Definitions
Single View of the Customer
Data
Metadata Architecture`
Managed Services
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Application Management
ERP
Help Desk
Hosting
Quality Assurance &
Testing
Enterprise Mobility
 Mobile Roadmap & Strategy
 Mobile App Development (HTML5,iOS,Android,
Windows
 Mobile Testing & Security
Test Maturity Improvements
Managed Testing Services
IV&V
Test Centers of Excellence
Automation and Automated
Regression
 Technical Support of Testing
 Independent Test Organization
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5. 3
Trends Today

6. 6
Trends Today
 CFO is increasingly involved in IT
 Close to 50% of IT Leaders report to the CFO; 25%
report to the CMO
 63% of CFOs plan upgrades to BI in the coming year
 92% of CFOs DO NOT believe IT provides
transformational or differentiation capabilities
 Analytics today continues to overwhelmingly rely on
lagging KPI indicators rather than Predictive
Analytics even as more and better tools are
available for moving to predictive models

7. 7
Trends Today
CFOs indicate new applications for Financial
Governance are needed
 Cloud and Mobile are cited as opportunities BUT
91.8% of all devices connecting to the web were PCs
in 2012 – only 5.2% were smartphones, and 2.5% were
tablets; Predictions for 2014 are over 80% of devices
connecting will still be PCs
 Big Data is a much talked about subject BUT 90% of
Big Data projects are failures

8. 8
BI Investment
Needs

9. 9
CFO 2013
Initiatives

10. 10
2014 Enterprise
Initiatives

11. 11
Initiatives in
Finance Today

12. 12
The Data Dilemma

13. The Data Dilemma
13
EXECS
CUSTOMERS
COMPETITORS
MARKET
EXTERNAL
DATA
DILEMMA
ACCTG
PRODUCTION
INVENTORY
MSG
EXP/DB
FILES
INTERNAL
MANAGE ~ ANALYSIS ~ PLANNING
INTEGRATE
MANAGE
LEVERAGE
$

14. 14
Making sense of it all
Clarity of purpose
!
Definition of scope
Allocation of resources
Concrete result expectations
!
Comparative Analytical Measures (e.g. KPIs)
!
Rationalization of measures into actionable items
and hierarchical groups
Defining predictive analytics workspaces

15. 15
Metadata and Contextual Analysis

16. 16
Social Network Diagram
• Contextual analytics is one of the hottest areas of interest
pertaining to big data today
• Smart companies know there is tremendous value in contextual
analytics. But aggregating, categorizing, summarizing, exploring
and contextualizing unstructured data is a big undertaking.

17. 17
Contextualization
 Context is the interrelated conditions in which something exists or
occurs . Helping define context is Environment, Setting, Timeline, Genre
 Why is context important?
 Consistency needed in returned result sets
 The context describes the internal or external “framework”
 Internal contextual information is crucial
 External contextual information is knowledge that which cannot be gotten from
the text of the item itself
 Time and resources are wasted in searching irrelevant and non-material
information

18. 18
Business Goals

19. 19
Vision to Execution Road Map Alignment
3-Year IT Vision – Business Plans
Business
Context
FY 1 Plan
FY 2 Plan
FY 3 Plan
IT Strategies; Business Outcomes with Target Results; Goals and Objectives;
Risks; Business Capabilities; Key Stakeholders; Committed Investments;
Financial Models
Business Outcome Milestones
IT Context
Planning &
Analysis
IT Business Model,
IT Services Portfolio,
Major
Initiatives/Programs, IT
Operating Model,
Performance Target
Results
Enterprise Architecture
IT Services
Applications
Common Services
Processes
Data / Information
Infrastructure Shared
Services (SIS)
Updates to FY 1 plus
New Capabilities,
IT Services,
Portfolios Refresh, and
Performance Targets
Updates to FY 1 plus
New Capabilities,
IT Services,
Portfolios Refresh, and
Performance Targets
ISS FY Planning
SIS Catalog
App Services
Compute
Storage
Content Delivery
Support Services
Networking
Deployment
Management & Monitoring
EIM & Enterprise Endpoints
Security Management
Project portfolios sync'd to ISS releases
Cross-stack road maps
Reference architectures & solution
patterns
Standards & principles
Currency schedules & road maps
Budgeting/investment/resource profiles
AppDev, security and integration
guidelines

20. 20
Align Change/Development/Investment
to Business Outcome Milestones
Business Solutions
Approach
Business Unit
Approach
Architecture
Approach
People
BU 1
Application
Process
BU 2
Infrastructure
Technology
BU n
Cloud Services
Information
Region 1
Information
Governance
Region 2
IT Services
Money
Corporate
Shared Services
Business
Processes
Manageme
nt
Technical Shared
Services
Supply Chain
Partners
Business Outcome
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Milestone (final or interim)
Functional Milestone within a level of
analysis (depends on approach)

21. 21
In Order to Realize New Opportunities, You Need to
Think Beyond Traditional Sources of Data
Transactional and
Application Data
Machine Data
Social Data
Enterprise
Content
 Volume
 Velocity
 Variety
 Variety
 Structured
 Semi-structured
 Highly unstructured
 Highly unstructured
 Throughput
 Ingestion
 Veracity
 Volume

22. 22
Framework for A BI Support Center

23. 23
An Analytics Framework
Integration
User Interfaces
Databases
Visualization
Dev Tools
Application
Accelerators
• Spreadsheet-style
visualization for data
discovery & exploration
Content
Management
• Built-in IDE & admin consoles
• Enterprise-class security
Engine Components
Map Reduce +
Indexing
Workload Mgmt
Security
Apache Hadoop
• Unique text analytic engines
Admin Console
Accelerators
Text
Analytics
• Performance & workload
optimizations
Information
Governance
• High-speed connectors to
integration with other
systems/sources
• High availability
• Machine data and social data
analytics accelerators

24. 24
Data
Exploration
 Discover the Data
 Provide discovery and navigation
 Connect securely to applications that manage
data—regardless of location
 Leave big data in place
File
Systems
Application/
Users
 Identify the value of the data
 Recognize users of the data
 Establish context of data usage
Commenting
Tagging
Data Explorer Platform
 Analyze Structured and
Unstructured Data
 Visualize relationships and
reveal themes
Relational
Data
Content
Management
Hadoop
System
Stream
Computing
Data
Warehouse
ERP
Cloud
 Create personalized views of the data
Systems
Management
Supply
Chain
Shared
Folders
 Augment the data with user knowledge
Application
Development
Accelerators
CRM
RSS Feeds
Social Tools
Visualization
& Discovery
Email
Rating
 Collaborate on the Data
Big Data Platform
Custom
Sources
External
Sources
 Identify ongoing user and system integration
points
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Information Integration & Governance

25. 25
Analytics and Predictive Analytics

26. 26
Historical Analytics
 Presentation of historical data
 Dashboards, Drill-downs, interactive reports, static reports
 New methods and devices
 Identifying the metrics that affect key objectives
 Synchronizing those metrics through an organization
 Creating user tools to show effects of good (and bad) choices
 Tying the financial, operational, and sales worlds together
 Analyzing to predict the future
 Refining models for accuracy

27. 27
Predictive Analytics
 Manipulation of data
 Dashboards, Drill-downs, interactive reports
 New methods and devices
 Varying the metrics that affect key objectives
 Synchronizing the impact of metrics through an organization
 Creating user tools to show effects of good (and bad) choices
 Tying the financial, operational, and sales worlds together
 Creating models that show potential future scenarios
 Refining models for accuracy using advanced tools and statistics

28. 28
Governance Issues

29. 29
Issues
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Data Quality
Repeatability
Best Fit Models
Naming Consistency
Formula Consistency
Calculation Consistency
Plugging Numbers
(see Pentagon*)

30. 30
Sample Product stacks

31. 31

32. 32
Analytics Toolscape

33. 33
A Vendor Sample Toolbox
 Data Exploration: Visual data exploration to quickly understand and
analyze data within the database
 OLAP Optimization: Built-in multidimensional analytics optimization
 Geospatial: Native in-database geospatial data types and analytics
 Temporal: Native in-database temporal support to manage and
update time data and analytics
 Advanced Analytics: Optimized in-database data mining
technology from leading vendors, open source, and Teradata
 Agile Analytics: In-database data labs to accelerate exploration of
new data and ideas
 Big Data Integration: Partner tools to analyze unstructured and
structured data
 Application Development: Tools and techniques to accelerate
development of in-database and Hadoop analytics

34. 34
Sample Big Data Appliances
IBM - Pure Analytics (Netezza)
Oracle – Exodata, Exalytics
SAP – Hana
Teradata
Other vendors entering the marketplace
Sample Toolkits
IBM – InfoSphere, Open source, Hadoop
Oracle – Hyperion, OBIEE, Hadoop
SAP – BO, BPC
Teradata – Vision, Hadoop
SAS – BI Software, BI Analytics, Visual Analytics
Tibco – Spotfire

35. Thank You
35
rgristak@ciber.com
720 326 9422